{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 8\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:24:55.941199Z","iopub.execute_input":"2023-05-27T09:24:55.942161Z","iopub.status.idle":"2023-05-27T09:25:15.067996Z","shell.execute_reply.started":"2023-05-27T09:24:55.942117Z","shell.execute_reply":"2023-05-27T09:25:15.066443Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:15.073762Z","iopub.execute_input":"2023-05-27T09:25:15.076930Z","iopub.status.idle":"2023-05-27T09:25:15.808607Z","shell.execute_reply.started":"2023-05-27T09:25:15.076888Z","shell.execute_reply":"2023-05-27T09:25:15.807042Z"},"trusted":true},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"torch.Size([8])\n","output_type":"stream"}]},{"cell_type":"markdown","source":"LKA","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\nfrom functools import partial\n\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\nfrom timm.models.registry import register_model\nfrom timm.models.vision_transformer import _cfg\nimport math\n\nclass Mlp(nn.Module):\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)\n        self.dwconv = DWConv(hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)\n        self.drop = nn.Dropout(drop)\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n        elif isinstance(m, nn.Conv2d):\n            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n            fan_out //= m.groups\n            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))\n            if m.bias is not None:\n                m.bias.data.zero_()\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.dwconv(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\n\n\nclass LKA(nn.Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)\n        self.conv_spatial = nn.Conv2d(dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)\n        self.conv1 = nn.Conv2d(dim, dim, 1)\n\n\n    def forward(self, x):\n        u = x.clone()        \n        attn = self.conv0(x)\n        attn = self.conv_spatial(attn)\n        attn = self.conv1(attn)\n\n        return u * attn\n","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:22.470333Z","iopub.execute_input":"2023-05-27T09:25:22.471275Z","iopub.status.idle":"2023-05-27T09:25:23.876252Z","shell.execute_reply.started":"2023-05-27T09:25:22.471234Z","shell.execute_reply":"2023-05-27T09:25:23.875250Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_lka=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_lka:\n            self.lka = LKA(planes)\n        else:\n            self.lka = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.lka is None:\n            out = self.lka(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_lka=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_lka:\n            #self.lka = LKA(planes * 4, 16)\n            self.lka = LKA(planes * 4)\n        else:\n            self.lka = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.lka is None:\n            out = self.lka(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_lka=att_type == \"LKA\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_lka=att_type == \"LKA\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:26.417203Z","iopub.execute_input":"2023-05-27T09:25:26.417617Z","iopub.status.idle":"2023-05-27T09:25:26.464842Z","shell.execute_reply.started":"2023-05-27T09:25:26.417583Z","shell.execute_reply":"2023-05-27T09:25:26.459992Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"#device=\"cpu\"\ndevice=\"cuda\"\nnet=ResidualNet(\"CIFAR100\",18,100,\"LKA\").to(device)\n# net=ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(net)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:34.748338Z","iopub.execute_input":"2023-05-27T09:25:34.749246Z","iopub.status.idle":"2023-05-27T09:25:38.949274Z","shell.execute_reply.started":"2023-05-27T09:25:34.749212Z","shell.execute_reply":"2023-05-27T09:25:38.948152Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (lka): LKA(\n        (conv0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=64)\n        (conv_spatial): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=64)\n        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (lka): LKA(\n        (conv0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=64)\n        (conv_spatial): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=64)\n        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (lka): LKA(\n        (conv0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=128)\n        (conv_spatial): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=128)\n        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (lka): LKA(\n        (conv0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=128)\n        (conv_spatial): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=128)\n        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (lka): LKA(\n        (conv0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)\n        (conv_spatial): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=256)\n        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (lka): LKA(\n        (conv0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=256)\n        (conv_spatial): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=256)\n        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (lka): LKA(\n        (conv0): Conv2d(512, 512, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=512)\n        (conv_spatial): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=512)\n        (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (lka): LKA(\n        (conv0): Conv2d(512, 512, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=512)\n        (conv_spatial): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(9, 9), dilation=(3, 3), groups=512)\n        (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n      )\n    )\n  )\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:50.741419Z","iopub.execute_input":"2023-05-27T09:25:50.742392Z","iopub.status.idle":"2023-05-27T09:25:50.753130Z","shell.execute_reply.started":"2023-05-27T09:25:50.742350Z","shell.execute_reply":"2023-05-27T09:25:50.751159Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"5000\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n\ndef validate(lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:53.019048Z","iopub.execute_input":"2023-05-27T09:25:53.019414Z","iopub.status.idle":"2023-05-27T09:25:53.033314Z","shell.execute_reply.started":"2023-05-27T09:25:53.019383Z","shell.execute_reply":"2023-05-27T09:25:53.032289Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"lossv = []\ntop1AccuracyList = []   # top1准确率列表\ntop5AccuracyList = []   # top5准确率列表\nmax_epoch = 20\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    train(epoch)\n    with torch.no_grad():\n        validate(lossv,top1AccuracyList,top5AccuracyList)\n        \nprint('Finished Training')","metadata":{"execution":{"iopub.status.busy":"2023-05-27T09:25:56.626631Z","iopub.execute_input":"2023-05-27T09:25:56.627074Z","iopub.status.idle":"2023-05-27T10:35:22.399730Z","shell.execute_reply.started":"2023-05-27T09:25:56.627039Z","shell.execute_reply":"2023-05-27T10:35:22.398406Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 5000/5000 [03:14<00:00, 25.75it/s, loss=2.77]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.9606, Top1Accuracy: 2670/10000 (27%) Top5Accuracy: 5635.0/10000 (56%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  44%|████▎     | 2187/5000 [01:24<01:45, 26.70it/s, loss=2.93]IOPub message rate exceeded.\nThe notebook server will temporarily stop sending output\nto the client in order to avoid crashing it.\nTo change this limit, set the config variable\n`--NotebookApp.iopub_msg_rate_limit`.\n\nCurrent values:\nNotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\nNotebookApp.rate_limit_window=3.0 (secs)\n\nEpoch 1: 100%|██████████| 5000/5000 [03:11<00:00, 26.16it/s, loss=2.15] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3973, Top1Accuracy: 3814/10000 (38%) Top5Accuracy: 7004.0/10000 (70%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2:  11%|█         | 550/5000 [00:20<02:56, 25.26it/s, loss=2.21] IOPub message rate exceeded.\nThe notebook server will temporarily stop sending output\nto the client in order to avoid crashing it.\nTo change this limit, set the config variable\n`--NotebookApp.iopub_msg_rate_limit`.\n\nCurrent values:\nNotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\nNotebookApp.rate_limit_window=3.0 (secs)\n\nEpoch 2: 100%|██████████| 5000/5000 [03:11<00:00, 26.06it/s, loss=2.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1443, Top1Accuracy: 4371/10000 (44%) Top5Accuracy: 7499.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 5000/5000 [03:11<00:00, 26.14it/s, loss=1.17] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 1.9645, Top1Accuracy: 4803/10000 (48%) Top5Accuracy: 7820.0/10000 (78%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 5000/5000 [03:10<00:00, 26.23it/s, loss=0.62]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0950, Top1Accuracy: 4768/10000 (48%) Top5Accuracy: 7826.0/10000 (78%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 5: 100%|██████████| 5000/5000 [03:10<00:00, 26.21it/s, loss=1.4]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3195, Top1Accuracy: 4812/10000 (48%) Top5Accuracy: 7698.0/10000 (77%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 6: 100%|██████████| 5000/5000 [03:11<00:00, 26.17it/s, loss=0.724]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6487, Top1Accuracy: 4723/10000 (47%) Top5Accuracy: 7590.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 7: 100%|██████████| 5000/5000 [03:10<00:00, 26.18it/s, loss=0.429]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.8830, Top1Accuracy: 4656/10000 (47%) Top5Accuracy: 7577.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 8: 100%|██████████| 5000/5000 [03:09<00:00, 26.33it/s, loss=0.49]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0420, Top1Accuracy: 4661/10000 (47%) Top5Accuracy: 7534.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 9: 100%|██████████| 5000/5000 [03:14<00:00, 25.73it/s, loss=0.45]    \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.2012, Top1Accuracy: 4709/10000 (47%) Top5Accuracy: 7527.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 10: 100%|██████████| 5000/5000 [03:14<00:00, 25.72it/s, loss=0.0505]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.2288, Top1Accuracy: 4704/10000 (47%) Top5Accuracy: 7501.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 11: 100%|██████████| 5000/5000 [03:14<00:00, 25.74it/s, loss=0.0176]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.2427, Top1Accuracy: 4764/10000 (48%) Top5Accuracy: 7481.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 12: 100%|██████████| 5000/5000 [03:14<00:00, 25.66it/s, loss=0.0113]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.4403, Top1Accuracy: 4616/10000 (46%) Top5Accuracy: 7367.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 13: 100%|██████████| 5000/5000 [03:13<00:00, 25.82it/s, loss=0.483]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.3143, Top1Accuracy: 4717/10000 (47%) Top5Accuracy: 7466.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 14: 100%|██████████| 5000/5000 [03:14<00:00, 25.67it/s, loss=0.0861]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.6356, Top1Accuracy: 4623/10000 (46%) Top5Accuracy: 7384.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 15: 100%|██████████| 5000/5000 [03:21<00:00, 24.77it/s, loss=0.122]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.5658, Top1Accuracy: 4630/10000 (46%) Top5Accuracy: 7449.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 16: 100%|██████████| 5000/5000 [03:15<00:00, 25.57it/s, loss=0.0276]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.6289, Top1Accuracy: 4606/10000 (46%) Top5Accuracy: 7410.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 17: 100%|██████████| 5000/5000 [03:15<00:00, 25.63it/s, loss=0.0466]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.6461, Top1Accuracy: 4678/10000 (47%) Top5Accuracy: 7440.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 18: 100%|██████████| 5000/5000 [03:14<00:00, 25.67it/s, loss=0.0011]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.8221, Top1Accuracy: 4603/10000 (46%) Top5Accuracy: 7340.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 19: 100%|██████████| 5000/5000 [03:14<00:00, 25.71it/s, loss=0.209]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.7325, Top1Accuracy: 4687/10000 (47%) Top5Accuracy: 7431.0/10000 (74%)\n\nFinished Training\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n# 绘制曲线\nplt.figure(figsize=(5, 3))\nplt.plot(np.arange(1, max_epoch + 1), lossv)\nplt.title('validation loss')\n\nplt.figure(figsize=(5,3))\nplt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\nplt.title('validation top1 accuracy')\n\nplt.figure(figsize=(5,3))\nplt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\nplt.title('validation top5 accuracy')","metadata":{"execution":{"iopub.status.busy":"2023-05-27T10:36:43.523067Z","iopub.execute_input":"2023-05-27T10:36:43.523535Z","iopub.status.idle":"2023-05-27T10:36:44.276535Z","shell.execute_reply.started":"2023-05-27T10:36:43.523491Z","shell.execute_reply":"2023-05-27T10:36:44.275503Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'validation top5 accuracy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x300 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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vvqqucuhFvAYGRFRE1avXg2NRoPf//735i6FWsFjZEREN9mzZw/Onz+PN998E1OnTkWPHj3MXRK1gkFGRHSTN954AwcPHsTw4cPx4YcfmrscagMeIyMiIqvGY2RERGTVGGRERGTVLO4YmVarRU5ODlxdXds9QzYREVk/IQTKy8sREBAAqbT5dpfFBVlOTk6HzXFHRETWLzMzE4GBgc0+bnFBpps8NTMzs8PmuiMiIuujUqkQFBTU6uVzLC7IdN2JSqWSQUZERK0eZuJgDyIismoMMiIismoMMiIismoMMiIismoMMiIismoMMiIismoMMuoQZdV1OJNVCnW9xtylEFEXY3HnkVHrqmrrkZhyHQHujujj5woHe5lJX18IgaySaiSll+DYtWIkpZcgJb8cQgDdXBX4w4hQPD4kBC4KfryIqPNZ3GVcVCoV3NzcUFZWxhOim7Fk81msO5oBAJBKgFBvZ9wR4Ia+/q7o669EP38lfFwVHTZXZb1Gi4t55Th+rRjH0kuQdK0EeaqaRus5yWWoqm1okSkd7PDksB6YPawHvFwUHVIHEXUtbc0DBpmVKapQI+btPait18LDyR4lVXVNruflLMcdAUr09Veir78r7vB3Q08fZ9jLWu9NrlTX41Rmqb61dSK9BJW1hl2GdlIJ+nV3w10hHri7hweiQzzh5miPrSez8UliKq4WVgIAHOylmHl3MP4wIhSBHk63vwOIqMtgkNmo/+6+jP/svIQBgW7YGjcc18vVSM5V4UKuChdyy3E+pwxphZXQNvGuymVShPu64A5/XcApcYe/EjX1Ghy/VoLj6cU4fq0E53NV0NyyAVeFHQaFeOCuEA/c1cMTA4Pc4ShvuktToxXYkZyH5QmpOJtdBqAh+B4cGIDnRoWht2/L86YREQEMMpukrtfgnn/uxfVyNT6YORBTBnZvcr3qWg0u5Zfj/I2AO5+jwsW8clSo69v8Wt3dHRF9o7V1Vw9P9PZ1hUxqXFelEAIHrhRhReIVHLhSpF9+3x2+mDc6DIOCPYzaHnU8IQTOZpchMeU6okM8MKyXt7lLItJjkNmgzSeysOjb0/BVKvDrn8ZAbtf2QadabcMAjfO5ZTifW47zOQ0hl11aDYkE6OunxF03QuuuEA8EuDt2aO2nM0uxIiEV8efzoPvEDe3piedH98KIcG9ee86E6jVaHL1WjB3J+diRnIecsobjnRIJMH9sOF4aEw6pkX+0EHUGBpmNEULggQ/3IzlHhVfG9UHcvb06ZLtl1XWQSSUmG2F4paACKxNTseVkNupvdF9Gdldi3qheGB/pZ3Srj9qmpk6D/ZcLEZ+ch10X8g2OrTrJZejrr0RSegkA4N4+PnhvxkC4O8nNVS4RAAaZzTmaVoxHVx6Cwk6KQ0vGwtPZur9kckqr8fmvaVh3NAPVdQ0DSUK9nfHsyJ6YNqg7FHamPaXAFqlq6rD3YgHik/OQkHJdP6IUANyd7BHb1xfj+/nhnnBvONjLsCkpC3/Zchbqei2CPB3xye+i0S/AzYw/AXV1DDIb89zXSfglOQ+PDQ7CsoeizF1OhymprMXqg9fw1aFrKL3RSujmqsDs4T0w7c7u8Hfr2C5OW3e9XI2d5/MRn5yHg6mFqNP89uvt7+aAcf38cH8/Xwzu4Qm7Jkawnssuw7w1ScgsrobCToq3pvXH9Ojmr8xL1JkYZDYks7gKo97dC60AdiwcaZOj/irV9Vh3NAOf/5qmP0dNIgGGhHpi6sDumBDpDzcnezNXefuEEMgurUZtvRZyOynkdlIoZDLY20kgl0mbDJfWZBZXIT45D7+cy0NSRglu/o0O83HGuH5+GNfPD1GBbm06FllaVYsFG04hIeU6AOD3Q0Pw6gN3GHVM9nZotALbTmVjeUIqfFwUeHFML8SEefE4ahfEILMh//jhPD7fn4YR4d74eu4Qc5fTqWrrtdh2Khsbj2fh6LVi/XK5TIrRfXww9c7uGBPRzeSzmbSXEAIZxVU4lFqEQ1eLcPhqEfJV6mbXl0oAuZ0U9jIpFHZSyGVS/X1d8OmWyWVS5JTV4EKuymAbUYFu+vDq1c2lXXVrtQIf7L6M/+65DCGAO4PdsfyJQZ3aQhZCYM/FArzzSwpS8ssNHru7hwfmj+2N4b0YaF0Jg8xGVKjrEfPWbpSr67Fq9t24N6KbuUsymaySKmw/nYttp7JxMe+3LzZXhR3GRfph6sDuiAnzsrgBIlklNwVXapF+VKCOvUwCB3sZauu1qNVocbu/gTKpBIN7eGJcP1/c38+vQ0ec7r1YgPnrT0JVUw9vFzk+fGwQYsK8Omz7OsevFeOfv1zEsWsNA06UDnZ4dlQYrpersfZoBmrrtQCA6BAPLIgNxz29ONLVkmm1AlcLK9v9h5QOg8xGrD6Qhr9tP4+ePs7YtXBUlx0WfTFPhW2ncvD9qRxkl1brl/u4KjA5KgBT7wxA/+5t6zrraHllNTh0tVAfXpnF1QaP28skGBjkjpieXhja0wuDQjz0LUohBOq1AnUabUOw1Wuhrtc23L9p2a3/163vYC/DiHCfTh38k1FUhWe/ScKFXBVkUgkWj++Dp0f07JB9nZJXjnfjL2LXhQIAgMJOitnDe+D5Ub30Xcn5qhp8kpiKtUcyoL4RaIOC3TE/tjdG8tQN/UTdljBAqqZOg80nsvHF/qvILavBoSVj4ebY/kMCDDIboNUK3PvvBKQXVeHvUyPx+6Eh5i7J7LRagePpJdh2Khs/ns3VDxABgJ7ezpgysDumDAxAD2/nTquhoLwGh1KLcPhqMQ5fLULajem4dGRSCaIC3RDT0wsxYV6IDvGAk9y6J1CurtXgL1vOYvPJbADAxP5+eOfhAe0+bSOrpAr/2XkJW05mQ4iGffboXYGYP7Y3/NwcmnxOgaoGnyRexZoj6fpAGxjkjgWx4RjV26dLBZoQAkfTirExKQs/nc2FVghM6h+Ax4cEYVCwh8n3RUF5Db45lI5vjmSguLIWAOCisMOns6IxLKz9J9kzyGzArvP5+MP/jkPpYIfDfx5r9V+GHa22Xot9l65j66ls7LqQj5o6rf6xAUHumDowAA9EBcDbRY46zU2tHs0tLZ8mWjy3/r9Oo0V6URUOphYi9bphcEklQGT3huAaGuaFu3t42uTM/0IIfHMkA29sT0adRiDMxxkrfx+NXt3aPvioqEKNj/em4pvD6ajVNLxfE/v74eX7+yDMp23dUAXlNfg08Sq+OZKuf88HBLljwdhwjO5j24GWW1aN75KysCkpC9eKqppcJ7ybCx4bHIyHBnXv9HMBL+Sq8MX+NHx/Kkf/fnZ3d8Sc4T0w4+4guDrc3gAtBpkNePyzwziYWoRnR/XEkgl9zV2ORatQ12NHch62nsrB/svXDeaalEhw28ehbqabCSUmzAsxPb1wd6jnbXWfWJsTGSV4/psTyFPVwFkuw7uPDMDE/v4tPqdSXY/Pf03DZ79e1U+VNizMC4vHR2BAkHu76rhersan+1Lx9eGbAi3QDS+NDceYiG42E2g1dRrsupCPb49n4dfL1/WfZWe5DA9EBeDRuwMBSLD+aAa2n8nR7wu5nRQTI/0wc3AwhoR6dtj+0GoFEi9dxxf707D/SqF++aBgd/xhRE/cf4dvu0bfNoVBZuUu5Kow4YNfIZNKsO9P96J7B08ZZcuul6vx45kcbD2Vg1OZpY0el0klBqMBFbeMBrSXSW6MEJRBfuNxe5kEns4KDA71xNCenl1+1ovCCjVeWHsCh682jCx9dmRPvDKuT6MvsNp6LdYdzcCHey6jsKKhy6lfgBKLx0d02NRkhRVqfLbvKv53KF1/cn3/7m6YPzYcY/taZ6AJIZCco8K3xzOx7VQOyqp/60IfEuqJR+4KwsT+fo16aVQ1ddh2KgfrjmTg/E2jWXt6O2Pm4CBMHxTY7ssq3Xz8S9crIZUAE/r7Y+49oZ0ydyqDzMr9adNpfHs8C5Oi/PHx44PMXY7VKqmsRb1WGASVpY1ytFb1Gi3ejU/Byn1XATTMnfnR44Pg7aKAVivw/ekc/Htnin7wS4iXE16+vw8e6O/fKYOWiirU+PTXq/j6ULp+FpPI7kq8NCYc993h22ygabUC6notauo0qK7T3PRvw7Kb71fXaeAslyHQwwlBno7o5urQoZ+nogo1tp7KwcbjmQYjdf3dHPBwdCAejg5EiFfrx391k0GvO5qB70/l6C/DZC+T4P5+fnh8cDBienq16X0oKK/B14fS8c3hdP3UZq4KO8y4OwhPDuuBIM/OuzwTg8yKFVaoMezGNce+mzcM0SGcJZ4s109nc/HKxtOorNXAT+mAuDG9sOZwuv6L2NtFgfmx4Zh5d1Cbrod3u4ora/HZr1fxv4PX9F/gPX2c4eZoj+paDdT1WlTXalBTr9Hfby97mQTd3R31wRbo4YRAj9/u+7i0foHbeo0W+y5fx7fHsrD7Yr5+Nha5nRT33+GLR+8KwvBe3u0OzAp1PbafzsH6oxk4nVWmXx7s6YSZg4PwcHQgurk2HmDT1PGvQA9HzBkeikfvCrzt419twSCzYh/suoz3dl3CgCB3bH1+mFV2jVDXcqWgHM9+nWQwEMZVYYfnRodhzvAeZhmoVFxZi89/vYqvbgq01shlUijspXC0l8HBXnbjXykUN/2/vKYemSVVyCmtaXTdvlsp7KQGwRbo4YSgG2FnL5Pi+9M52HwiCwXlv50k37+7Gx65KxAPDgjo8C7s5JwyrD+aia0ns1F+41ilnVSC2L6+mDk4CPf08savlwvx+f6rBpdeig7xwNx7Qjv0+FdbMMislLpeg+Fv70VhRcvXHCOyNBXqeizZfBa7L+TjiSHBeH50L3hYwOTWJZW1OJJWBKlEAke5YUA5GASWzKhWT71GizxVDbJKqpFZXNXwb0nDv1nFVchV1bR5kJGHkz2m3RmIR+4KRF//zv/eq6qtx49ncrHuaAZOZJTqlzvay/THGWVSCcZH+nXa8a+2YJBZqe+SsvDyxoZrju1fPMYkXTFEHUkIwV4ENAx0yS2rbjLoMourUFpVh3vCvfHoXYEYE+Frsrksb5WSV451RzOw+UQWVDX1cFXYYebghuNfgR6dd/yrLdqaB7Z3sosVE0LgywNpAIBZMT0YYmSVGGIN5HZShHg5Nzs4w1ICv4+fK/72YD/834QIJOeo0NvXxSTHvzoSg8yCHE0rRnKOCgo7KR4fHGzucoioE1lCiN3MwV5mtQPL+Ce/BdG1xh4aFGgRxxaIiKwBg8xCZBRVYcf5fADAU8N7mLcYIiIrwiCzEF8dugYhgBHh3gi3wQtnEhF1ltsKsrfffhsSiQQLFizQLxs9ejQkEonB7bnnnrvdOm1aeU0dNhzLBADMvSfUzNUQEVmXdg/2OHbsGFauXImoqKhGjz399NN444039PednMw7hNPSbUrKQoW6HmE+zhgZ7mPucoiIrEq7WmQVFRV44okn8Nlnn8HDo/EoFycnJ/j5+elvXfF8sLbSaAVWH7wGAJgzPLTLXjiTiKi92hVkcXFxmDRpEmJjY5t8fM2aNfD29kZkZCSWLFmCqqqmr5sDAGq1GiqVyuDWley5WID0oiq4OdrjoUGcxYOIyFhGdy2uX78eJ06cwLFjx5p8/PHHH0dISAgCAgJw5swZLF68GCkpKdi8eXOT6y9btgyvv/66sWXYjC/3Nwy5f2xwMC+cSUTUDkZ9c2ZmZmL+/PnYuXMnHByavhz5M888o/9///794e/vj7FjxyI1NRVhYWGN1l+yZAkWLVqkv69SqRAUFGRMWVbrfI4Kh64WQSaVYFZMiLnLISKySkYFWVJSEgoKCjBo0G/Xx9JoNNi3bx8++ugjqNVqyGQyg+cMGTIEAHDlypUmg0yhUEChaN+F3qyd7gToCZF+COCFM4mI2sWoIBs7dizOnj1rsGzOnDmIiIjA4sWLG4UYAJw6dQoA4O/f8qXQu5rr5Wp8fyoHAPAUh9wTEbWbUUHm6uqKyMhIg2XOzs7w8vJCZGQkUlNTsXbtWkycOBFeXl44c+YMFi5ciJEjRzY5TL8rW3MkHbUaLQYGuZvtEglERLagQ0cXyOVy7Nq1C++//z4qKysRFBSE6dOn469//WtHvozVU9dr8M3hdABsjRER3a7bDrKEhAT9/4OCgpCYmHi7m7R520/norCiFn5KB0yI9DN3OUREVo1zLZqYEEI/5H7WsBBec4yI6DbxW9TEjqQV43yuCg72Ujx2N685RkR0uxhkJqZrjfGaY0REHYNBZkIZRVXYeYHXHCMi6kgMMhNafbDhmmOjevugVzdec4yIqCMwyEykUl2Pb483XHOMQ+6JiDoOg8xEdl3IR4W6HiFeThgZ7m3ucoiIbAaDzES2n84FAEyOCoBEwmuOERF1FAaZCZRV12HfpesAgAcGcM5JIqKOxCAzgZ3n81Gr0aJXNxf08eUgDyKijsQgM4EfzjTMcv9AlD+7FYmIOhiDrJOVVNZi/+VCAMADUQFmroaIyPYwyDpZfHIe6rUCEX6u6NXNxdzlEBHZHAZZJ/vx7I3RigPYGiMi6gwMsk5UVKHGwdQiAA3Hx4iIqOMxyDrRz+fyoNEK9O/uhhAvZ3OXQ0Rkkxhknejm0YpERNQ5GGSdpEBVgyNpxQCASQwyIqJOwyDrJD+dzYUQwJ3B7gj0cDJ3OURENotB1kl+ONMwWpHnjhERdS4GWSfIKa3G8fQSAMDE/n5mroaIyLYxyDrBTzfOHbu7hwf83RzNXA0RkW1jkHWC7exWJCIyGQZZB8ssrsLpzFJIJcAEdisSEXU6BlkH0w3yGBLqhW6uDmauhojI9jHIOtiPZ2+cBM0LaBIRmQSDrANdK6zEuWwVZFIJJkQyyIiITIFB1oF0U1INC/OCp7PczNUQEXUNDLIOpDs+NpmjFYmITIZB1kGuFJTjYl457GUSjOvH0YpERKbCIOsg2083tMZGhPvAzcnezNUQEXUdDLIOIITgJVuIiMyEQdYBLuaVI/V6JeR2Utx3h6+5yyEi6lIYZB1A1xob1dsHrg7sViQiMiUG2W1q6FbUza3IbkUiIlNjkN2mc9kqpBdVwcFeiti+7FYkIjI1Btlt0nUrjonoBmeFnZmrISLqehhkt8GwW5EnQRMRmcNtBdnbb78NiUSCBQsW6JfV1NQgLi4OXl5ecHFxwfTp05Gfn3+7dVqkU5mlyC6thpNchnv7dDN3OUREXVK7g+zYsWNYuXIloqKiDJYvXLgQ27dvx8aNG5GYmIicnBw89NBDt12oJdK1xmL7+sJRLjNzNUREXVO7gqyiogJPPPEEPvvsM3h4eOiXl5WV4YsvvsB//vMfjBkzBtHR0Vi1ahUOHjyIw4cPd1jRlkCrFfiRoxWJiMyuXUEWFxeHSZMmITY21mB5UlIS6urqDJZHREQgODgYhw4danJbarUaKpXK4GYNkjJKkKeqgavCDqP6+Ji7HCKiLsvoYXbr16/HiRMncOzYsUaP5eXlQS6Xw93d3WC5r68v8vLymtzesmXL8Prrrxtbhtn9cLphtOJ9/XyhsGO3IhGRuRjVIsvMzMT8+fOxZs0aODg4dEgBS5YsQVlZmf6WmZnZIdvtTBqtwE/nGoKZl2whIjIvo4IsKSkJBQUFGDRoEOzs7GBnZ4fExET897//hZ2dHXx9fVFbW4vS0lKD5+Xn58PPr+lLmygUCiiVSoObpTuSVoTr5Wq4OdpjeC9vc5dDRNSlGdW1OHbsWJw9e9Zg2Zw5cxAREYHFixcjKCgI9vb22L17N6ZPnw4ASElJQUZGBmJiYjquajPTjVYc388PcjueikdEZE5GBZmrqysiIyMNljk7O8PLy0u/fO7cuVi0aBE8PT2hVCrx4osvIiYmBkOHDu24qs2oXqPFLze6FR8YwNGKRETm1uFzKr333nuQSqWYPn061Go1xo0bh+XLl3f0y5jNwdQiFFfWwtNZjpieXuYuh4ioy7vtIEtISDC47+DggI8//hgff/zx7W7aIunmVhwf6Qc7GbsViYjMjd/ERqitv6lbkSdBExFZBAaZEQ5cKYSqph4+rgoMCWW3IhGRJWCQGWH7jW7FiZF+kEklZq6GiIgABlmb1dRpsDO5YRb/BwbwJGgiIkvBIGujfZeuo1xdDz+lA6KDPVp/AhERmQSDrI10J0FPivKHlN2KREQWg0HWBtW1Guy6cKNbkaMViYgsCoOsDfamFKCqVoNAD0cMDHI3dzlERHQTBlkb6E6CnhTlD4mE3YpERJaEQdaKSnU99lwsAMBLthARWSIGWSt2XchHTZ0WPbyc0C/A8i8xQ0TU1TDIWrHrQkNrjN2KRESWiUHWipQ8FQDgrh6eZq6EiIiawiBrQZ1Gi7TCSgBAeDcXM1dDRERNYZC1IL2oEnUaASe5DAFujuYuh4iImsAga8Hl/AoADa0xzuZBRGSZGGQtuFzQEGS9urmauRIiImoOg6wFl/LLAQDhvjw+RkRkqRhkLbhyo0XWm0FGRGSxGGTNqNdocfW6bsQiuxaJiCwVg6wZ6cVVqNVo4WgvQ3d3jlgkIrJUDLJm6EYs9uKIRSIii8Yga8aVghsDPXgiNBGRRWOQNeOSrkXGgR5ERBaNQdYM3TlkvTnQg4jIojHImqDRCqRevzGrB1tkREQWjUHWhIziKtTWa6GwkyLQw8nc5RARUQsYZE24fGNGj17dXCDjiEUiIovGIGuC7vgYRywSEVk+BlkTLuvnWORADyIiS8cga8Jvs96zRUZEZOkYZLfQaMVNkwWzRUZEZOkYZLfIKqmCul4LuZ0UQR6cY5GIyNIxyG6hm2Oxp7cz7GTcPURElo7f1Le4zG5FIiKrwiC7xWVOFkxEZFUYZLfQdS1yaioiIuvAILuJ9qYRizyHjIjIOhgVZCtWrEBUVBSUSiWUSiViYmLw888/6x8fPXo0JBKJwe25557r8KI7S3ZpNarrNLCXSRDiyTkWiYisgZ0xKwcGBuLtt99GeHg4hBD46quvMGXKFJw8eRL9+vUDADz99NN444039M9xcrKeQNAdH+vp7cIRi0REVsKoIJs8ebLB/TfffBMrVqzA4cOH9UHm5OQEPz+/jqvQhHh8jIjI+rS72aHRaLB+/XpUVlYiJiZGv3zNmjXw9vZGZGQklixZgqqqqha3o1aroVKpDG7m8ttkwTw+RkRkLYxqkQHA2bNnERMTg5qaGri4uGDLli244447AACPP/44QkJCEBAQgDNnzmDx4sVISUnB5s2bm93esmXL8Prrr7f/J+hAv00WzBYZEZG1kAghhDFPqK2tRUZGBsrKyrBp0yZ8/vnnSExM1IfZzfbs2YOxY8fiypUrCAsLa3J7arUaarVaf1+lUiEoKAhlZWVQKpVG/jjtJ4RAv6XxqKrVYNeikejFVhkRkVmpVCq4ubm1mgdGt8jkcjl69eoFAIiOjsaxY8fwwQcfYOXKlY3WHTJkCAC0GGQKhQIKhcLYMjpcTlkNqmo1sJNKEOLlbO5yiIiojW57aJ5WqzVoUd3s1KlTAAB/f//bfZlOd+lGt2KotzPsOWKRiMhqGNUiW7JkCSZMmIDg4GCUl5dj7dq1SEhIQHx8PFJTU7F27VpMnDgRXl5eOHPmDBYuXIiRI0ciKiqqs+rvMFfyOcciEZE1MirICgoKMGvWLOTm5sLNzQ1RUVGIj4/Hfffdh8zMTOzatQvvv/8+KisrERQUhOnTp+Ovf/1rZ9XeoXTnkPFimkRE1sWoIPviiy+afSwoKAiJiYm3XZC5XOI5ZEREVokHg9AwYvEKzyEjIrJKDDIAeaoaVKjrIZNKEOrNEYtERNaEQYbfuhV7eDlBbsddQkRkTfitjZtm9GC3IhGR1WGQAfrjY7050IOIyOowyPDbydC9eA4ZEZHV6fJBJoS4adZ7tsiIiKxNlw+ygnI1ymvqIZUAPX04YpGIyNp0+SC7rB+x6AyFnczM1RARkbG6fJDpj4+xW5GIyCp1+SC7XMDJgomIrFmXD7IrBbwqNBGRNevSQSaE0M/qwa5FIiLr1KWD7HqFGmXVdZBKgDAfBhkRkTXq0kGmu5hmsKcTHOw5YpGIyBp16SD7bcQiB3oQEVmrLh1klznHIhGR1WOQgSMWiYisWZcNMiEEL99CRGQDumyQFVXWoqSqDhKOWCQismpdNsh0cywGeTjBUc4Ri0RE1qrrBpluRg+eCE1EZNW6bpDpZvTgQA8iIqvWdYPsRousNwd6EBFZta4bZPkcek9EZAu6ZJAVVahRVFkLgCMWiYisXZcMsis3ToQO9HCEs8LOzNUQEdHt6JJBdkk3owdHLBIRWb0uGWRXdDN68KrQRERWr0sG2WW2yIiIbEaXDLJL+hGLbJEREVm7LhdkJZW1KKxQAwB6sUVGRGT1ulyQXbne0Brr7u4IF45YJCKyel0uyPRTU7E1RkRkE7pckF3K52TBRES2pMsFme5k6N4c6EFEZBO6XJDpJgvmrPdERLahSwVZWXUd8lUcsUhEZEuMCrIVK1YgKioKSqUSSqUSMTEx+Pnnn/WP19TUIC4uDl5eXnBxccH06dORn5/f4UW315UbrTE/pQOUDvZmroaIiDqCUUEWGBiIt99+G0lJSTh+/DjGjBmDKVOmIDk5GQCwcOFCbN++HRs3bkRiYiJycnLw0EMPdUrh7cFLtxAR2R6jTqSaPHmywf0333wTK1aswOHDhxEYGIgvvvgCa9euxZgxYwAAq1atQt++fXH48GEMHTq046puJ/2MHryYJhGRzWj3MTKNRoP169ejsrISMTExSEpKQl1dHWJjY/XrREREIDg4GIcOHWp2O2q1GiqVyuDWWXQDPdgiIyKyHUYH2dmzZ+Hi4gKFQoHnnnsOW7ZswR133IG8vDzI5XK4u7sbrO/r64u8vLxmt7ds2TK4ubnpb0FBQUb/EG3129B7BhkRka0wOsj69OmDU6dO4ciRI5g3bx6efPJJnD9/vt0FLFmyBGVlZfpbZmZmu7fVElVNHXLLagAAvXzYtUhEZCuMnmxQLpejV69eAIDo6GgcO3YMH3zwAWbMmIHa2lqUlpYatMry8/Ph5+fX7PYUCgUUCoXxlRtJ1xrr5qqAmxNHLBIR2YrbPo9Mq9VCrVYjOjoa9vb22L17t/6xlJQUZGRkICYm5nZf5rZdyeeMHkREtsioFtmSJUswYcIEBAcHo7y8HGvXrkVCQgLi4+Ph5uaGuXPnYtGiRfD09IRSqcSLL76ImJgYCxmxeGNGD54ITURkU4wKsoKCAsyaNQu5ublwc3NDVFQU4uPjcd999wEA3nvvPUilUkyfPh1qtRrjxo3D8uXLO6VwY+mvCs2BHkRENkUihBDmLuJmKpUKbm5uKCsrg1Kp7LDtDn97D7JLq7HxuRjc3cOzw7ZLRESdo6150CXmWqxQ1yO7tBoA0MuHLTIiIlvSJYJMN2LR20UBD2e5mashIqKO1CWC7PKNgR48EZqIyPZ0jSDTDfTgiEUiIpvTNYJMN/Se55AREdmcrhFkujkW2SIjIrI5Nh9kVbX1yCppGLEYzhYZEZHNsfkg041Y9HKWw5MjFomIbI7NB5nuqtCcmoqIyDbZfpAVcLJgIiJbZvtBls+rQhMR2TLbD7ICdi0SEdkymw6y6loNMkuqALBrkYjIVtl0kKVer4AQgIeTPbw4YpGIyCbZdJBdLrhxfKybKyQSiZmrISKizmDbQZbPi2kSEdk6mw6yS/mcLJiIyNbZdJBd0XUtcqAHEZHNstkgq6nTIKO4YcQiuxaJiGyXzQZZ6vUKaAXg5mgPHxeFucshIqJOYrNBduWmi2lyxCIRke2y2SD7bcQij48REdkymw2yS7o5FjlikYjIptlskOm7FjnQg4jIptlkkKnrNbhWVAmAcywSEdk6mwyyq9croRWAq4MdurlyxCIRkS2zM3cBnUFdr8Wdwe5wdbDniEUiIhtnk0E2MMgdW54fbu4yiIjIBGyya5GIiLoOBhkREVk1BhkREVk1BhkREVk1BhkREVk1BhkREVk1BhkREVk1izuPTAgBAFCpVGauhIiIzEmXA7pcaI7FBVl5ecOs9UFBQWauhIiILEF5eTnc3NyafVwiWos6E9NqtcjJyYGrq6tVTS+lUqkQFBSEzMxMKJVKc5fTZqzb9Ky1dtZtetZae0fVLYRAeXk5AgICIJU2fyTM4lpkUqkUgYGB5i6j3ZRKpVV94HRYt+lZa+2s2/SstfaOqLullpgOB3sQEZFVY5AREZFVY5B1EIVCgaVLl0KhsK7rn7Fu07PW2lm36Vlr7aau2+IGexARERmDLTIiIrJqDDIiIrJqDDIiIrJqDDIiIrJqDLI2WLZsGe6++264urqiW7dumDp1KlJSUlp8zurVqyGRSAxuDg4OJqq4wd/+9rdGNURERLT4nI0bNyIiIgIODg7o378/fvrpJxNVa6hHjx6NapdIJIiLi2tyfXPt73379mHy5MkICAiARCLB1q1bDR4XQuC1116Dv78/HB0dERsbi8uXL7e63Y8//hg9evSAg4MDhgwZgqNHj5qs7rq6OixevBj9+/eHs7MzAgICMGvWLOTk5LS4zfZ83jq6dgCYPXt2ozrGjx/f6nbNuc8BNPl5l0gkePfdd5vdpin2eVu+/2pqahAXFwcvLy+4uLhg+vTpyM/Pb3G77f3daAqDrA0SExMRFxeHw4cPY+fOnairq8P999+PysrKFp+nVCqRm5urv6Wnp5uo4t/069fPoIb9+/c3u+7Bgwfx2GOPYe7cuTh58iSmTp2KqVOn4ty5cyasuMGxY8cM6t65cycA4JFHHmn2OebY35WVlRgwYAA+/vjjJh9/55138N///heffPIJjhw5AmdnZ4wbNw41NTXNbnPDhg1YtGgRli5dihMnTmDAgAEYN24cCgoKTFJ3VVUVTpw4gVdffRUnTpzA5s2bkZKSggcffLDV7RrzeeuM2nXGjx9vUMe6deta3Ka59zkAg3pzc3Px5ZdfQiKRYPr06S1ut7P3eVu+/xYuXIjt27dj48aNSExMRE5ODh566KEWt9ue341mCTJaQUGBACASExObXWfVqlXCzc3NdEU1YenSpWLAgAFtXv/RRx8VkyZNMlg2ZMgQ8eyzz3ZwZcabP3++CAsLE1qttsnHLWF/AxBbtmzR39dqtcLPz0+8++67+mWlpaVCoVCIdevWNbudwYMHi7i4OP19jUYjAgICxLJly0xSd1OOHj0qAIj09PRm1zH289YRmqr9ySefFFOmTDFqO5a4z6dMmSLGjBnT4jrm2Oe3fv+VlpYKe3t7sXHjRv06Fy5cEADEoUOHmtxGe383msMWWTuUlZUBADw9PVtcr6KiAiEhIQgKCsKUKVOQnJxsivIMXL58GQEBAejZsyeeeOIJZGRkNLvuoUOHEBsba7Bs3LhxOHToUGeX2aLa2lp88803eOqpp1qcSNoS9vfN0tLSkJeXZ7BP3dzcMGTIkGb3aW1tLZKSkgyeI5VKERsba9b3oaysDBKJBO7u7i2uZ8znrTMlJCSgW7du6NOnD+bNm4eioqJm17XEfZ6fn48ff/wRc+fObXVdU+/zW7//kpKSUFdXZ7D/IiIiEBwc3Oz+a8/vRksYZEbSarVYsGABhg8fjsjIyGbX69OnD7788kts27YN33zzDbRaLYYNG4asrCyT1TpkyBCsXr0av/zyC1asWIG0tDSMGDFCf6mcW+Xl5cHX19dgma+vL/Ly8kxRbrO2bt2K0tJSzJ49u9l1LGF/30q334zZp4WFhdBoNBb1PtTU1GDx4sV47LHHWpwA1tjPW2cZP348/ve//2H37t345z//icTEREyYMAEajabJ9S1xn3/11VdwdXVttXvO1Pu8qe+/vLw8yOXyRn/ktLT/2vO70RKLm/3e0sXFxeHcuXOt9kPHxMQgJiZGf3/YsGHo27cvVq5cib///e+dXSYAYMKECfr/R0VFYciQIQgJCcG3337bpr/0LMUXX3yBCRMmICAgoNl1LGF/26K6ujo8+uijEEJgxYoVLa5rKZ+3mTNn6v/fv39/REVFISwsDAkJCRg7dqzJ6rgdX375JZ544olWByyZep+39fvP1NgiM8ILL7yAH374AXv37jX6UjP29va48847ceXKlU6qrnXu7u7o3bt3szX4+fk1GmmUn58PPz8/U5TXpPT0dOzatQt/+MMfjHqeJexv3X4zZp96e3tDJpNZxPugC7H09HTs3LnT6MtxtPZ5M5WePXvC29u72TosaZ8DwK+//oqUlBSjP/NA5+7z5r7//Pz8UFtbi9LSUoP1W9p/7fndaAmDrA2EEHjhhRewZcsW7NmzB6GhoUZvQ6PR4OzZs/D39++ECtumoqICqampzdYQExOD3bt3GyzbuXOnQUvH1FatWoVu3bph0qRJRj3PEvZ3aGgo/Pz8DPapSqXCkSNHmt2ncrkc0dHRBs/RarXYvXu3Sd8HXYhdvnwZu3btgpeXl9HbaO3zZipZWVkoKipqtg5L2ec6X3zxBaKjozFgwACjn9sZ+7y177/o6GjY29sb7L+UlBRkZGQ0u//a87vRWpHUinnz5gk3NzeRkJAgcnNz9beqqir9Or///e/F//3f/+nvv/766yI+Pl6kpqaKpKQkMXPmTOHg4CCSk5NNVvfLL78sEhISRFpamjhw4ICIjY0V3t7eoqCgoMmaDxw4IOzs7MS//vUvceHCBbF06VJhb28vzp49a7Kab6bRaERwcLBYvHhxo8csZX+Xl5eLkydPipMnTwoA4j//+Y84efKkfnTf22+/Ldzd3cW2bdvEmTNnxJQpU0RoaKiorq7Wb2PMmDHiww8/1N9fv369UCgUYvXq1eL8+fPimWeeEe7u7iIvL88kddfW1ooHH3xQBAYGilOnThl85tVqdbN1t/Z5M0Xt5eXl4o9//KM4dOiQSEtLE7t27RKDBg0S4eHhoqamptnazb3PdcrKyoSTk5NYsWJFk9swxz5vy/ffc889J4KDg8WePXvE8ePHRUxMjIiJiTHYTp8+fcTmzZv199vy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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}